A method of assigning processing resources is described. The method includes receiving an application and analyzing the application to determine an expected run time use of the processing resources. At least a portion of the processing resources are assigned to the application based on the expected run time use of the processing resources. A computing system architecture including a processing resource pool, an interface, and a special purpose optimization coprocessor is also described. The interface receives an application to be executed. The special purpose optimization coprocessor receives information about the application and the processing resource pool and outputs an allocation scheme for allocating tasks of the application to the processing resource pool.
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2. The method of claim 1, wherein the application includes a plurality of tasks and wherein the analyzing the application further includes: determining a number of the plurality of tasks; determining an order of the plurality of tasks; and determining an information transfer between tasks in the plurality of tasks.
This invention relates to analyzing software applications to optimize task execution. The problem addressed is the inefficiency in task scheduling and resource allocation within applications, leading to suboptimal performance. The solution involves a method for analyzing an application that includes multiple tasks, where the analysis determines the number of tasks, their execution order, and the information transfer between them. By understanding these factors, the system can improve task scheduling, reduce bottlenecks, and enhance overall application performance. The analysis may also identify dependencies between tasks, allowing for better resource allocation and parallel processing where possible. This method is particularly useful in complex applications where task interactions significantly impact efficiency. The invention aims to provide a structured approach to task analysis, enabling developers to optimize application workflows and improve execution speed.
3. The method of claim 1, wherein the receiving the application further includes: receiving a plurality of applications; wherein the analyzing the application further includes analyzing the plurality of applications to determine the expected run time use of the processing resources for each application of the plurality of applications; and wherein the assigning further includes assigning the at least the portion of the processing resources to each application based on the expected run time use of the processing resources for each application.
This invention relates to resource allocation in computing systems, specifically optimizing the distribution of processing resources among multiple applications. The problem addressed is inefficient resource allocation, where processing resources are either underutilized or overloaded, leading to performance degradation or wasted capacity. The invention provides a method for dynamically assigning processing resources to multiple applications based on their expected runtime usage. The method involves receiving multiple applications and analyzing each to determine the expected runtime use of processing resources for each application. This analysis considers factors such as computational demands, expected execution time, and resource requirements. Based on this analysis, the method assigns at least a portion of the available processing resources to each application in proportion to their expected runtime use. This ensures that resources are allocated efficiently, preventing overloading of any single application while maximizing overall system performance. The method may also include monitoring resource usage in real-time and adjusting allocations dynamically to adapt to changing demands. This approach improves system efficiency, reduces bottlenecks, and enhances the performance of concurrent applications.
4. The method of claim 1, further comprising: analyzing the processing resources; and wherein the assigning further assigns the at least the portion of the processing resources to the application based on the analyzing of the processing resources.
This invention relates to dynamic resource allocation in computing systems, specifically addressing the challenge of efficiently distributing processing resources among multiple applications to optimize performance. The method involves monitoring and analyzing the available processing resources, such as CPU, memory, or other computational capabilities, to determine their current utilization and capacity. Based on this analysis, the system dynamically assigns at least a portion of these resources to a specific application, ensuring that the allocation aligns with the application's requirements and the system's overall performance goals. The analysis step evaluates factors such as resource availability, application demand, and system constraints to make informed allocation decisions. This dynamic approach prevents resource underutilization or overcommitment, improving efficiency and responsiveness in multi-application environments. The method may also involve reallocating resources in real-time as conditions change, ensuring continuous optimization. By integrating resource analysis with allocation, the system adapts to varying workloads, enhancing overall system performance and reliability.
5. The method of claim 4, wherein the processing resources include a plurality of cores and wherein the analyzing the processing resources further includes: determining a plurality of latencies between the plurality of cores; determining resource contention costs for each of the plurality of cores; and wherein the assigning is based on the plurality of latencies and the resource contention costs.
This invention relates to optimizing task scheduling in multi-core processing systems to improve performance by reducing latency and resource contention. The method analyzes processing resources, including multiple cores, to determine inter-core latencies and resource contention costs. By evaluating these factors, tasks are assigned to cores in a way that minimizes communication delays and contention for shared resources. The approach involves measuring the time delays between different cores and assessing how heavily each core is utilized, then using this data to allocate tasks efficiently. This ensures that tasks requiring frequent communication are placed on cores with lower latency connections, while heavily utilized cores are avoided to prevent bottlenecks. The goal is to enhance overall system performance by dynamically balancing workload distribution based on real-time resource conditions. This method is particularly useful in high-performance computing environments where efficient resource management is critical.
7. The computing system architecture of claim 6, wherein the special purpose optimization coprocessor further includes: a plurality of digital oscillators; and at least one programmable interconnect configured to provide weights for and to selectably couple at least a portion of the plurality of digital oscillators.
The computing system architecture involves a specialized optimization coprocessor designed to enhance computational efficiency in solving complex optimization problems. The coprocessor includes a network of digital oscillators, which are configurable elements that generate periodic signals. These oscillators are interconnected through a programmable interconnect system, allowing dynamic coupling and weight adjustments between them. The interconnect system enables selective coupling of subsets of oscillators, facilitating the formation of different network topologies tailored to specific optimization tasks. By adjusting the weights applied to the connections, the system can fine-tune the interactions between oscillators to improve convergence toward optimal solutions. This architecture is particularly useful in applications requiring real-time optimization, such as machine learning, signal processing, and control systems, where traditional computational methods may be too slow or resource-intensive. The programmable interconnect and oscillator network provide flexibility in adapting the coprocessor to various problem domains, enhancing its versatility and performance.
8. The computing system architecture of claim 6, further comprising: a processor configured to: analyze the application to determine an expected run time use of the processing resource pool; and wherein the information received by the special purpose optimization coprocessor further includes the expected run time use of the processing resource pool.
A computing system architecture is designed to optimize the allocation and utilization of processing resources in a shared processing resource pool. The system addresses the challenge of efficiently managing processing resources in environments where multiple applications compete for computational power, leading to potential inefficiencies or bottlenecks. The architecture includes a special purpose optimization coprocessor that receives information about the processing resource pool, including its current state and available resources. This coprocessor dynamically adjusts the allocation of processing resources to applications based on real-time demands and system conditions. Additionally, the system analyzes applications to predict their expected runtime use of the processing resource pool, incorporating this forecast into the optimization process. By integrating expected runtime usage data, the coprocessor can make more informed decisions about resource allocation, improving overall system performance and resource utilization. The architecture ensures that processing resources are allocated in a way that balances immediate needs with future requirements, reducing waste and enhancing efficiency.
9. The computing system architecture of claim 8, wherein to analyze the application, the processor is configured to: determine a number of the tasks; determine an order of the tasks; and determine an information transfer between the tasks; and wherein the expected run time use of the processing resource pool includes the number of the tasks, the order of the tasks and the information transfer between the tasks.
This invention relates to computing system architectures designed to optimize resource allocation for executing applications. The problem addressed is the inefficient use of processing resources in computing systems, particularly when running applications with multiple interdependent tasks. Traditional systems often fail to accurately predict resource requirements, leading to underutilization or overloading of processing resources. The architecture includes a processor configured to analyze an application by determining the number of tasks within the application, the order in which these tasks are executed, and the information transfer required between tasks. This analysis is used to predict the expected runtime use of a processing resource pool, which includes the number of tasks, their execution order, and the data dependencies between them. By understanding these factors, the system can dynamically allocate processing resources to ensure optimal performance and efficiency. The architecture may also include a resource pool manager that adjusts resource allocation based on the analysis, ensuring that tasks are executed in the most efficient manner while minimizing idle time or bottlenecks. This approach improves overall system performance by aligning resource allocation with the actual demands of the application.
10. The computing system architecture of claim 8 wherein the processor is further configured to: map the information about the application and the processing resource pool to an optimization problem.
The computing system architecture relates to optimizing resource allocation in a distributed computing environment. The problem addressed is inefficient utilization of processing resources in large-scale computing systems, where applications may not be optimally matched with available processing resources, leading to performance bottlenecks or underutilization. The architecture includes a processor configured to analyze application requirements and processing resource capabilities, then dynamically allocate resources based on this analysis. The processor further maps the collected information about the application and the processing resource pool to an optimization problem, allowing for the determination of an optimal or near-optimal allocation of resources. This optimization problem may involve constraints such as resource availability, application priorities, or performance targets. By solving this problem, the system ensures that applications are assigned to processing resources in a way that maximizes efficiency, minimizes latency, or meets other specified objectives. The architecture may also include mechanisms for monitoring resource usage and adjusting allocations in real-time to adapt to changing workloads or system conditions. The goal is to improve overall system performance, reduce operational costs, and enhance scalability in distributed computing environments.
11. The computing system architecture of claim 6, wherein the interface further receives a plurality of applications; and wherein the allocation scheme output by the special purpose optimization coprocessor allocates the tasks of each of the plurality of applications to the processing resource pool.
This invention relates to computing system architectures designed to optimize task allocation across a processing resource pool. The problem addressed is the inefficient distribution of computational tasks, particularly in systems running multiple applications, leading to suboptimal performance and resource utilization. The architecture includes a special purpose optimization coprocessor that generates an allocation scheme to distribute tasks from multiple applications across available processing resources. The system dynamically assigns tasks based on the optimization coprocessor's output, ensuring balanced and efficient resource usage. The optimization coprocessor may employ algorithms tailored to minimize latency, maximize throughput, or optimize energy consumption, depending on system requirements. The architecture also includes an interface that receives the applications and their associated tasks, which are then processed by the coprocessor to generate the allocation scheme. This approach improves system performance by dynamically adapting to varying workloads and application demands, reducing bottlenecks and enhancing overall efficiency. The solution is particularly useful in high-performance computing environments where multiple applications compete for processing resources.
12. The computing system architecture of claim 11, wherein the interface receives the plurality of applications in a time interval; and wherein the special purpose optimization coprocessor continuously updates the allocation scheme in the time interval.
The computing system architecture is designed for optimizing resource allocation in a multi-application environment. The system addresses the challenge of efficiently distributing computational resources among multiple applications to maximize performance and minimize conflicts. The architecture includes a special purpose optimization coprocessor that dynamically adjusts resource allocation based on real-time demands. The interface of the system receives multiple applications within a defined time interval, and the optimization coprocessor continuously updates the allocation scheme during this interval. This ensures that resources are allocated in a way that adapts to changing application requirements, improving overall system efficiency. The coprocessor may use algorithms to prioritize applications, balance workloads, or allocate resources based on performance metrics. The system is particularly useful in environments where applications have varying and unpredictable resource demands, such as cloud computing, data centers, or embedded systems. By continuously updating the allocation scheme, the system avoids static configurations that may lead to inefficiencies or bottlenecks. The architecture may also include mechanisms to monitor application behavior and adjust allocations in real-time, ensuring optimal performance throughout the time interval.
13. The computing system architecture of claim 11, wherein the interface receives the plurality of applications in a time interval; and wherein the special purpose optimization coprocessor outputs the allocation scheme for the plurality of applications received in the time interval.
This invention relates to computing system architectures designed to optimize resource allocation for multiple applications. The problem addressed is the inefficient distribution of computing resources when multiple applications are running simultaneously, leading to suboptimal performance and resource utilization. The system includes a special purpose optimization coprocessor that dynamically allocates resources based on the requirements of the applications. The interface receives a plurality of applications within a defined time interval and the optimization coprocessor generates an allocation scheme tailored to those applications. This scheme ensures that resources such as CPU, memory, and I/O are allocated in a way that maximizes efficiency and performance. The system is particularly useful in environments where multiple applications with varying resource demands must coexist, such as cloud computing, data centers, or embedded systems. The optimization coprocessor may use machine learning or heuristic algorithms to determine the best allocation strategy, considering factors like application priority, resource constraints, and historical performance data. The goal is to minimize resource contention and maximize throughput while ensuring that each application meets its performance requirements. This approach improves overall system efficiency and reduces the likelihood of bottlenecks or resource starvation.
14. The computing system architecture of claim 6, wherein the processing resource pool includes a plurality of cores; and wherein the special purpose optimization coprocessor further outputs the allocation scheme based on a plurality of latencies between the plurality of cores and resource contention costs for each of the plurality of cores.
This invention relates to computing system architectures designed to optimize resource allocation in multi-core processing environments. The problem addressed is inefficient task distribution across cores, leading to suboptimal performance due to latency and resource contention. The system includes a processing resource pool with multiple cores and a special purpose optimization coprocessor that generates an allocation scheme for tasks. The coprocessor considers both the latencies between cores and the resource contention costs associated with each core when determining the optimal allocation. By analyzing these factors, the system dynamically assigns tasks to minimize delays and conflicts, improving overall computational efficiency. The architecture ensures that tasks are distributed in a way that balances workload and reduces bottlenecks, enhancing performance in multi-core environments. The optimization coprocessor acts as a dedicated unit to handle these computations, distinct from the general-purpose cores, allowing for real-time adjustments based on current system conditions. This approach is particularly useful in high-performance computing, real-time processing, and other applications where efficient resource utilization is critical.
15. The computing system architecture of claim 6, wherein the processing resource pool includes a plurality of servers; and wherein the special purpose optimization coprocessor further outputs the allocation scheme based on a plurality of latencies within each of the plurality of servers and between the plurality of servers and based on resource contention costs for each of the plurality of servers.
The computing system architecture involves a processing resource pool comprising multiple servers, optimized for efficient task allocation. The system addresses the challenge of optimizing resource allocation in distributed computing environments where latency and resource contention impact performance. A special purpose optimization coprocessor generates an allocation scheme that considers both internal latencies within each server and inter-server communication latencies. Additionally, the coprocessor accounts for resource contention costs, which represent the performance degradation caused by competing tasks sharing the same server resources. By analyzing these factors, the system dynamically assigns tasks to servers in a way that minimizes delays and maximizes resource utilization. This approach improves overall system efficiency, particularly in high-demand scenarios where multiple tasks compete for limited resources. The architecture is designed to adapt to varying workloads and network conditions, ensuring optimal performance across distributed computing environments.
17. The computer program product of claim 16, wherein the computer instructions for receiving the application further include computer instructions for: receiving a plurality of applications; wherein the computer instructions for analyzing the application further include computer instructions for analyzing the plurality of applications to determine the expected run time use of the processing resource pool for each the plurality of applications; wherein computer instructions for providing the information further include computer instructions for providing to the special purpose optimization coprocessor the expected runtime use of the processing resource pool for each of the plurality of applications, the allocation scheme allocates the tasks of each of the plurality of applications to the processing resource pool based on the expected runtime use for each of the plurality of applications; and wherein the computer instructions for assigning the tasks further include computer instructions for assigning the tasks of each of the plurality of the applications using the allocation scheme.
This invention relates to optimizing task allocation in a processing resource pool for multiple applications. The problem addressed is efficiently distributing processing tasks among applications to maximize resource utilization and performance. The system receives multiple applications and analyzes each to determine their expected runtime use of the processing resource pool. This analysis includes evaluating how each application will utilize processing resources over time. The system then provides this expected runtime use data to a special-purpose optimization coprocessor, which generates an allocation scheme. The allocation scheme distributes tasks from each application across the processing resource pool based on their expected runtime use, ensuring balanced and efficient resource allocation. The system assigns tasks to the processing resource pool according to this scheme, optimizing performance by aligning task distribution with predicted resource demands. This approach improves processing efficiency by dynamically adjusting task allocation based on application-specific runtime requirements.
18. The computer program product of claim 16, further comprising: wherein the processing resource pool includes a plurality of cores in a server and memory resources for the plurality of cores.
This invention relates to a computer program product for managing a processing resource pool in a server environment. The technology addresses the challenge of efficiently allocating and utilizing computing resources, particularly in systems with multiple processing cores and shared memory resources. The invention provides a method for dynamically managing these resources to optimize performance and reduce overhead. The processing resource pool includes a plurality of cores within a server, along with associated memory resources dedicated to these cores. The system dynamically allocates tasks to the cores based on workload demands, ensuring that memory resources are efficiently utilized. This approach helps prevent resource contention and improves overall system efficiency by balancing the load across available cores and memory. The invention also includes mechanisms for monitoring resource usage and adjusting allocations in real-time. By dynamically reconfiguring the resource pool, the system can adapt to changing workloads, ensuring that critical tasks receive sufficient processing power and memory. This adaptive management reduces latency and enhances system responsiveness. Additionally, the system may include features for isolating certain tasks or processes to specific cores, improving security and reducing interference between different workloads. The memory resources are managed in a way that minimizes fragmentation and maximizes availability, further optimizing performance. Overall, this invention provides a scalable and efficient solution for managing processing and memory resources in multi-core server environments, addressing the need for dynamic resource allocation in modern computing systems.
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August 13, 2021
April 23, 2024
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